基于BP神经网络的离心泵关死点功率预测

离心泵关死点功率至今还不能通过理论计算求得。该文介绍了BP神经网络的结构和特点及其在离心泵性能预测领域的应用现状。基于BP神经网络建立了离心泵关死点功率的预测模型。给出了预测模型的输入模式,并应用试凑法确定了BP神经网络中间隐含层的数目。用46组数据该预测模型进行了训练并给出了神经网络权值和阈值,用3组数据该预测模型进行了仿真并对仿真结果进行了线性回归分析。研究结果表明:建立的离心泵关死点功率预测模型具有比较高的预测精度,其预测平均偏差为4%,可以应用于工程实践中离心泵关死点功率的理论求解。

Power prediction for centrifugal pumps at shut off condition based on BP neural network

At present, the power of centrifugal pumps at shut off condition can not be obtained by theory computation. The structure of the BP artificial neural network and its application situation in energy performance prediction of centrifugal pumps were introduced in detail. Based on BP artificial neural network, the characteristic prediction model is established to predict power of centrifugal pumps at shut off condition. The input mode of the BP network prediction model is presented and the number of middle layer is fixed by many tests. The characteristic data of 46 centrifugal pumps at shut off condition are used to train the network model, and the data of the other 3 centrifugal pumps are used to test the network model. The weight of each layer is also presented. The study fruits show that the prediction results of the model agree well with the experiment results. The average prediction discrepancy of the network is 4 percent, the minimum prediction discrepancy is 3.35 percent, and the maximal prediction discrepancy is 4.51 percent. The prediction precision of the BP network model can meet the engineering practical requirement.